A Pharmacokinetics-Neural Mass Model (PK-NMM) for the Simulation of EEG Activity during Propofol Anesthesia.

Liang Z, Duan X, Su C, Voss L, Sleigh J, Li X - PLoS ONE (2015)

Bottom Line:
The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen.The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects.The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

ABSTRACTModeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

pone.0145959.g005: Computational processing of simulated EEG.(A) Model predictions for the stationary states for he (blue line) and hi (red line). The superscript 0 in the legend represents stationary states. (B) The fluctuations of real-time he. The blue line in between is the stationary values for he. sEEG is calculated by using real-time he minus the values of he at stationary states. The fluctuations are displayed at 100*actual-size, and the line in between is the stationary state for excitatory neurons. The event time points are marked by dashed lines.

Mentions:
As previously described, the random fluctuations of excitatory neurons about their steady-state values are taken as the source of the scalp-measured EEG signal. Fig 5(A) shows the steady-state values for excitatory neurons (blue line) and inhibitory neurons (red line) for the same subject as used in Fig 4. The fluctuations of excitatory neurons along the steady-state curve are shown in Fig 5(B). In order to make the fluctuations visible, the amplitude is zoomed in 100 times, and the line in between is the stationary state for excitatory neurons.

pone.0145959.g005: Computational processing of simulated EEG.(A) Model predictions for the stationary states for he (blue line) and hi (red line). The superscript 0 in the legend represents stationary states. (B) The fluctuations of real-time he. The blue line in between is the stationary values for he. sEEG is calculated by using real-time he minus the values of he at stationary states. The fluctuations are displayed at 100*actual-size, and the line in between is the stationary state for excitatory neurons. The event time points are marked by dashed lines.

Mentions:
As previously described, the random fluctuations of excitatory neurons about their steady-state values are taken as the source of the scalp-measured EEG signal. Fig 5(A) shows the steady-state values for excitatory neurons (blue line) and inhibitory neurons (red line) for the same subject as used in Fig 4. The fluctuations of excitatory neurons along the steady-state curve are shown in Fig 5(B). In order to make the fluctuations visible, the amplitude is zoomed in 100 times, and the line in between is the stationary state for excitatory neurons.

Bottom Line:
The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen.The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects.The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.

ABSTRACTModeling the effects of anesthetic drugs on brain activity is very helpful in understanding anesthesia mechanisms. The aim of this study was to set up a combined model to relate actual drug levels to EEG dynamics and behavioral states during propofol-induced anesthesia. We proposed a new combined theoretical model based on a pharmacokinetics (PK) model and a neural mass model (NMM), which we termed PK-NMM--with the aim of simulating electroencephalogram (EEG) activity during propofol-induced general anesthesia. The PK model was used to derive propofol effect-site drug concentrations (C(eff)) based on the actual drug infusion regimen. The NMM model took C(eff) as the control parameter to produce simulated EEG-like (sEEG) data. For comparison, we used real prefrontal EEG (rEEG) data of nine volunteers undergoing propofol anesthesia from a previous experiment. To see how well the sEEG could describe the dynamic changes of neural activity during anesthesia, the rEEG data and the sEEG data were compared with respect to: power-frequency plots; nonlinear exponent (permutation entropy (PE)); and bispectral SynchFastSlow (SFS) parameters. We found that the PK-NMM model was able to reproduce anesthesia EEG-like signals based on the estimated drug concentration and patients' condition. The frequency spectrum indicated that the frequency power peak of the sEEG moved towards the low frequency band as anesthesia deepened. Different anesthetic states could be differentiated by the PE index. The correlation coefficient of PE was 0.80 ± 0.13 (mean ± standard deviation) between rEEG and sEEG for all subjects. Additionally, SFS could track the depth of anesthesia and the SFS of rEEG and sEEG were highly correlated with a correlation coefficient of 0.77 ± 0.13. The PK-NMM model could simulate EEG activity and might be a useful tool for understanding the action of propofol on brain activity.